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import logging
from sklearn.linear_model import LogisticRegression
from asreview.models.classifiers.base import BaseTrainClassifier
from asreview.models.classifiers.utils import _set_class_weight
[docs]class LogisticClassifier(BaseTrainClassifier):
"""
Logistic regression classifier (``logistic``).
The Logistic regressions classifier is an implementation based
on the sklearn Logistic regressions classifier.
Arguments
---------
C: float
Parameter inverse to the regularization strength of the model.
class_weight: float
Class weight of the inclusions.
random_state: int, RandomState
Random state for the model.
n_jobs: int
Number of CPU cores used.
"""
name = "logistic"
label = "Logistic regression"
def __init__(self, C=1.0, class_weight=1.0, random_state=None, n_jobs=1):
super(LogisticClassifier, self).__init__()
self.C = C
self.class_weight = class_weight
self.n_jobs = n_jobs
self._model = LogisticRegression(
solver="liblinear",
C=C,
class_weight=_set_class_weight(class_weight),
n_jobs=n_jobs,
random_state=random_state)
logging.debug(self._model)
[docs] def full_hyper_space(self):
from hyperopt import hp
hyper_choices = {}
hyper_space = {
"mdl_C": hp.lognormal("mdl_C", 0, 1),
"mdl_class_weight": hp.lognormal("mdl_class_weight", 0, 1),
}
return hyper_space, hyper_choices